Robot Rides June 25, 2011

One of Google's automated Prii with automotive radar and a laser range finder

Automated driving is the next massively disruptive technological wave in our future (I’d say that cell phones and the internet were the last two major waves). In 2004, 2005, and 2007, the Defense Advanced Research Projects Agency (DARPA) conducted a series of “Challenges.” Participants competed to create autonomous vehicles capable of driving in remote desert environments and in urban environments. Stanford’s “Stanley” SUV won the 2005 desert grand challenge and CMU’s “Boss” SUV won the 2007 urban challenge. OpenCV and ROS are direct descendants of Stanford’s efforts as are a variety of autonomous vehicle technologies that have changed the landscape of war in our most recent conflicts in Afghanistan and Iraq.

In the future, when our roads are populated with autonomous machines, traffic will be routed by globally aware optimization algorithms that have evolved to optimize bandwidth utilization in broadband and cellular networks. Congestion in cities will disappear. Travel times and gas consumption will dramatically decrease. And as with networks, people will be able to purchase higher priority traffic in order to reduce transit times even further. Ambulances and other emergency vehicles will be able to get to target locations much faster. Vehicular accidents and drunk driving incidents will disappear further increasing traffic throughput.

Parking will no longer be needed in cities as your vehicle will be able to drive elsewhere to park itself. You will also be able to subscribe to a pool of autonomous vehicles from which you can call a ride using a mobile phone app. Look out for your Google “G-car” subscriptions in the near future. It’ll redefine the meaning of “car pool.” You will no longer call a cab, you’ll call a car out of the “auto-cloud.”

Long distance travel will change dramatically when you can tell your vehicle to take you to grandma’s house. You will load your whole family inside your automated car, and have a good night’s sleep while your are driven the 8-12 hour trip, through the night, by a machine that requires no sleep. All for much less cost than a set of plane tickets.

As our roads become populated with autonomous vehicles, our personal lives will change dramatically. We will gain 10-20% of our waking hours otherwise spent focused on driving to and from work. We will be able to use this time for work and play. Stress levels will be dramatically reduced as individuals will no longer be focused on the trials and tribulations of the morning and evening rush hours. Road rage will disappear. I’m sure humans will find new things to stress about, but this major component of human unhappiness will be removed.

The How:

But what is keeping this technology from becoming a reality? It’s all about the world of artificial intelligence and machine cognition. Autonomous vehicles need to include a wealth of sensors and a huge volume of domain knowledge. Vehicles need advanced segmentation algorithms capable of understanding and extracting objects from the dynamic 3D world around them (algorithms that you learned when you were a child). In urban environments, once it can effectively segment out humans in its environment, it will need to be able to interpret their intentions. Will that kid chase out into the road after the ball? Does that human plan to cross the road or not? False negatives are not an option.

These algorithms are and will be extremely sophisticated. Furthermore, many of the algorithm components will be learned. It’s highly likely that the driving algorithms will spend quite a while simply watching professional drivers make choices in a broad variety of situations. Just like a teacher and a student, these algorithms will begin to generalize behaviors for given situations.

The intelligence of these systems will reside somewhere between a human adult and a horse, and we should mind that boundary carefully. Our autonomous vehicles will continue to increase their awareness (through access to richer sensors and live networked information databases), raw cognitive powers (as their computer cores follow Moore’s law), and their ability to intuit and generalize behaviors in given situations (as algorithms and training sets evolve). As these devices evolve, we must be careful that we don’t end up enslaving a race of self aware and internally motivated learning machines.